CN109388224A - A kind of power consumption optimization method of smart machine, system and smart machine - Google Patents
A kind of power consumption optimization method of smart machine, system and smart machine Download PDFInfo
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- CN109388224A CN109388224A CN201811126852.XA CN201811126852A CN109388224A CN 109388224 A CN109388224 A CN 109388224A CN 201811126852 A CN201811126852 A CN 201811126852A CN 109388224 A CN109388224 A CN 109388224A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
- G06F1/26—Power supply means, e.g. regulation thereof
- G06F1/32—Means for saving power
- G06F1/3203—Power management, i.e. event-based initiation of a power-saving mode
- G06F1/3234—Power saving characterised by the action undertaken
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Abstract
The present invention provides a kind of power consumption optimization method of smart machine, system and smart machines, are related to smart machine low-power consumption field.Its method includes: that smart machine obtains historical behavior operation of the user on the smart machine；Smart machine is operated according to historical behavior of the user on the smart machine, predicts behavior operation of the user in future time section；Smart machine is operated according to behavior of the user in future time section, controls the switch state on smart machine with behavior operation correlation function.In the present invention, smart machine will record the operation note history of forming behavior operation of lower user, according to the analysis operated to historical behavior, it can predict to obtain behavior operation of the user in future time section, the switch situation of correlation function is controlled with this, the optimised power consumption for realizing equipment, extend smart machine uses the time.
Description
Technical field
The present invention relates to smart machine low-power consumption field, espespecially a kind of power consumption optimization method of smart machine, system and intelligence
It can equipment.
Background technique
In today that science and technology continues to develop, various smart machines emerge one after another, such as mobile phone, tablet computer, intelligence
Energy wrist-watch, Intelligent bracelet etc..
Small in size due to these smart machines, space and the capacity that can configure battery are also relatively small, especially for
This skinny device of smartwatch, Intelligent bracelet, as function is more and more abundant, battery capacity has been increasingly difficult to meet and use
Demand, to keep battery life longer, reduce unnecessary battery consumption in the case where battery capacity is constant, save
Saving electricity becomes a kind of feasible scheme.
But in current optimised power consumption scheme, all users use same sets of plan, and the use habit of user is not
Together, the function point for consuming electricity is also different, and single user is with no personalization for the function being of little use to be deactivated, and battery is using simultaneously
The distribution not optimized, phone watch cell electricity must not get well good utilization.
Therefore for the power consumption of better Intelligent Optimal equipment, the invention proposes a kind of optimised power consumption sides of smart machine
Method, system and smart machine.
Summary of the invention
The object of the present invention is to provide a kind of power consumption optimization method of smart machine, system and smart machines, realize and set
Standby optimised power consumption, extend smart machine uses the time.
Technical solution provided by the invention is as follows:
A kind of power consumption optimization method the present invention provides smart machine includes:
Smart machine obtains historical behavior operation of the user on the smart machine；
The smart machine is operated according to historical behavior of the user on the smart machine, predicts user in future time
Behavior operation in section；
The smart machine is operated according to behavior of the user in future time section, control on smart machine with the behavior
Operate the switch state of correlation function.
In the present solution, user is when carrying out relevant operation in equipment, smart machine will record the operation note shape of lower user
It is operated at historical behavior, according to the analysis for operating historical behavior, can predict to obtain behavior of the user in future time section
Operation, the switch situation of correlation function is controlled with this.Such as user is predicted in 8 points of every morning or so and at 5 points in the afternoon
Left and right can open positioning function, then open positioning function in the two periods, and other periods close positioning function, are come with this
Reach reduction power consumption, saves the purpose of electric energy.
Optionally, the smart machine is operated according to historical behavior of the user on the smart machine, and prediction user exists
Behavior operation in future time section, specifically includes:
The smart machine is operated according to historical behavior of the user on the smart machine and hidden Markov mould
Type predicts behavior operation of the user in future time section.
It by hidden Markov model, can be operated according to historical behavior, accurately predict user in future time section
Interior behavior operation.
Optionally, the smart machine is operated according to behavior of the user in future time section, control on smart machine with
The switch state of the behavior operation correlation function, specifically includes:
The smart machine is operated according to behavior of the user in future time section, and systematic function switchs list, the function
It includes function title and opening time section, shut-in time section that list, which can be switched,；
The smart machine controls according to the functional switch list and operates correlation function with the behavior on smart machine
Switch state.
Preferably, further includes:
Historical behavior operation of the user on the smart machine is uploaded to server by the smart machine；
The server is operated according to behavior of the user in future time section, and systematic function switchs list, and will be described
Functional switch list is sent to another smart machine.
In the present solution, smart machine, which can also operate the historical behavior of user on intelligent devices, is uploaded to server,
It can be regarded as putting on record on the server, server can also be operated according to behavior of the user in future time section, generate function
List can be switched.When user has demand, another equipment can be sent to from server end by the functional switch list that prediction generates
In, low-power consumption work can be realized in another equipment.
The present invention also provides a kind of smart machines, comprising:
Behavior obtains module, for obtaining historical behavior operation of the user on the smart machine；
Behavior prediction module is operated according to historical behavior of the user on the smart machine, predicts user at future
Between behavior operation in section；
Functional control module, for being operated according to behavior of the user in future time section, control on smart machine with institute
State the switch state of behavior operation correlation function.
In the present solution, user is when carrying out relevant operation in equipment, smart machine will record the operation note shape of lower user
It is operated at historical behavior, according to the analysis for operating historical behavior, can predict to obtain behavior of the user in future time section
Operation, the switch situation of correlation function is controlled with this.Such as user is predicted in 8 points of every morning or so and at 5 points in the afternoon
Left and right can open positioning function, then open positioning function in the two periods, and other periods close positioning function, are come with this
Reach reduction power consumption, saves the purpose of electric energy.
Preferably, the behavior prediction module is also used to the historical behavior operation according to user on the smart machine,
And hidden Markov model, predict behavior operation of the user in future time section.
It by hidden Markov model, can be operated according to historical behavior, accurately predict user in future time section
Interior behavior operation.
Optionally, first list generation module, for being operated according to behavior of the user in future time section, systematic function
List is switched, the functional switch list includes function title and opening time section, shut-in time section；
The functional control module, for controlling and being grasped on smart machine with the behavior according to the functional switch list
Make the switch state of correlation function.
The present invention also provides a kind of optimised power consumption system of smart machine, the smart machine includes:
Behavior obtains module, for obtaining historical behavior operation of the user on the smart machine；
Behavior prediction module is operated according to historical behavior of the user on the smart machine, predicts user at future
Between behavior operation in section；
Functional control module, for being operated according to behavior of the user in future time section, control on smart machine with institute
State the switch state of behavior operation correlation function.
In the present solution, user is when carrying out relevant operation in equipment, smart machine will record the operation note shape of lower user
It is operated at historical behavior, according to the analysis for operating historical behavior, can predict to obtain behavior of the user in future time section
Operation, the switch situation of correlation function is controlled with this.Such as user is predicted in 8 points of every morning or so and at 5 points in the afternoon
Left and right can open positioning function, then open positioning function in the two periods, and other periods close positioning function, are come with this
Reach reduction power consumption, saves the purpose of electric energy.
Preferably, first list generation module, for being operated according to behavior of the user in future time section, systematic function
List is switched, the functional switch list includes function title and opening time section, shut-in time section；The function control mould
Block, for according to the functional switch list, controlling the switch state on smart machine with behavior operation correlation function.
Preferably, the smart machine further include:
Transmission module, for historical behavior operation of the user on the smart machine to be uploaded to server；
The optimised power consumption system of the smart machine further includes server, and the server includes:
Second list generation module, for being operated according to behavior of the user in future time section, systematic function switch train
Table, and the functional switch list is sent to another smart machine.
In the present solution, smart machine, which can also operate the historical behavior of user on intelligent devices, is uploaded to server,
It can be regarded as putting on record on the server, server can also be operated according to behavior of the user in future time section, generate function
List can be switched.When user has demand, another equipment can be sent to from server end by the functional switch list that prediction generates
In, low-power consumption work can be realized in another equipment.
Power consumption optimization method, system and the smart machine of a kind of smart machine provided through the invention, can bring with
It is lower at least one the utility model has the advantages that
In the present solution, user is when carrying out relevant operation in equipment, smart machine will record the operation note shape of lower user
It is operated at historical behavior, according to the analysis for operating historical behavior, can predict to obtain behavior of the user in future time section
Operation, the switch situation of correlation function is controlled with this.Such as user is predicted in 8 points of every morning or so and at 5 points in the afternoon
Left and right can open positioning function, then open positioning function in the two periods, and other periods close positioning function, are come with this
Reach reduction power consumption, saves the purpose of electric energy.
Smart machine can also by the historical behavior of user on intelligent devices operate be uploaded to server, it will be appreciated that for
Put on record on server, server can also be operated according to behavior of the user in future time section, and systematic function switchs list.
When user has demand, can be sent in another equipment from server end by the functional switch list that prediction generates, another equipment
Low-power consumption work can be realized.
Detailed description of the invention
Below by clearly understandable mode, preferred embodiment is described with reference to the drawings, to a kind of power consumption of smart machine
Above-mentioned characteristic, technical characteristic, advantage and its implementation of optimization method, system and smart machine are further described.
Fig. 1 is a kind of flow chart of one embodiment of the power consumption optimization method of smart machine of the present invention；
Fig. 2 is a kind of flow chart of another embodiment of the power consumption optimization method of smart machine of the present invention；
Fig. 3 is a kind of flow chart of another embodiment of the power consumption optimization method of smart machine of the present invention；
Fig. 4 is a kind of flow chart of another embodiment of the power consumption optimization method of smart machine of the present invention；
Fig. 5 is a kind of structural schematic diagram of one embodiment of smart machine of the present invention；
Fig. 6 is a kind of structural schematic diagram of another embodiment of smart machine of the present invention；
Fig. 7 is a kind of structural schematic diagram of one embodiment of the optimised power consumption system of smart machine of the present invention.
Drawing reference numeral explanation:
11- behavior acquisition module, 12- behavior prediction module, 13- functional control module, 14- first list generation module,
21- second list generation module.
Specific embodiment
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, Detailed description of the invention will be compareed below
A specific embodiment of the invention.It should be evident that drawings in the following description are only some embodiments of the invention, for
For those of ordinary skill in the art, without creative efforts, it can also be obtained according to these attached drawings other
Attached drawing, and obtain other embodiments.
To make simplified form, part related to the present invention is only schematically shown in each figure, they are not represented
Its practical structures as product.In addition, there is identical structure or function in some figures so that simplified form is easy to understand
Component only symbolically depicts one of those, or has only marked one of those.Herein, "one" is not only indicated
" only this ", can also indicate the situation of " more than one ".
The volume of the smart machines such as existing Intelligent bracelet, smartwatch is smaller, can configure space and the appearance of battery
Amount is also relatively small, and as function is more and more abundant, battery capacity has been increasingly difficult to meet the needs of using, and holds in battery
Measure it is constant in the case where, to keep battery life longer, reduce unnecessary battery consumption, save electricity become one kind can
Capable scheme.
But in current optimised power consumption scheme, the energy optimization scheme in some smart machines is divided into daytime and evening
Two periods, daytime open certain functions, have arrived and these functions are closed at night.User, which can choose, opens this energy optimization
Scheme achievees the purpose that device energy conservation.But the prioritization scheme of this scheme is relatively simple, due to user use habit not
Together, the function point for consuming electricity is also different, and smart machine is not operated according to the behavior of user, provides personalized energy-saving square
Case, battery use the distribution not optimized, and the battery capacity of smart machine is not utilized well.
Therefore, the present invention provides a kind of one embodiment of the power consumption optimization method of smart machine, as shown in Figure 1, packet
It includes:
S1 smart machine obtains historical behavior of the user on the smart machine and operates
Smart machine provided by the invention includes the intelligence such as smart phone, Intelligent flat computer, Intelligent bracelet, smartwatch
Equipment.When user uses these smart machines, smart machine will record the behavior operation of user, history of forming behavior operation note
Record.
Such as user has used positioning function in 09 second 30 minutes at 07 point of September in 2018 21 days, at 16 points of September in 2018 21 days
33 points also use positioning function in 04 second ...
Smart machine can be grasped in the behavior operation for predicting future time section from the historical behavior of user on intelligent devices
Middle acquisition historical behavior of noting down operates.
Smart machine described in S2 is operated according to historical behavior of the user on the smart machine, predicts user at future
Between behavior in section operate
Smart machine can get all operation notes in historical time section, including all functions of using, and it is every
One function is corresponding to use the time.Smart machine can predict user in the sexual behaviour of future time section according to these records
Operation.
Such as in user daily at 7 points to 8 points, 16 points to 17 points the two periods are all using positioning function, then may be used
User is obtained at 7 points to 8 points with prediction, and 16 points to 17 points the two periods can all use positioning function.
Smart machine described in S3 is operated according to behavior of the user in future time section, control on smart machine with the row
For the switch state for operating correlation function.
After predicting behavior operation of the user in future time section, i.e., the switch state of controllable correlation function.Example
If prediction obtains user at 7 points to 8 points, after 16 points to 17 points the two periods can all use positioning function, arrived whenever the time
Positioning function can be opened when up to 7, when then reaching at 8, positioning function can be closed, is opened again when reaching at 16 and positions function
Can, positioning function is closed when reaching at 17.It can be seen that only opening positioning function, phase there are two hour in one day time
Positioning function is opened for past 24 hours whole day, considerable battery level can be saved.
For another example the network operations such as wechat, webpage are all when in the historical behavior operation note of user on intelligent devices
When beginning to use after 18 points, then can predict that user in future time section, also can just use network after 18 points
Operating function, therefore before 18 points, can close the network communicating functions such as GPRS in the running on backstage, after 18 points,
The network communicating functions such as GPRS are just opened in the running on backstage.
In another example there is NFC on many smart phones now, (NearField Communication, near radio are logical
Letter) function, subway may be implemented by NFC function and swipe the card.Smart machine gets 8 points to 8 of morning of user's every workday
Thirty in the two periods of 6 pm to 6 thirty, can all use NFC function brush subway, then can predict following
8 points of the morning of every workday, the two periods of 6 pm to 6 thirty, user can use NFC function to 8 thirty.Cause
This, the every workday 8 points of morning to 8 thirty, the two periods of 6 pm to 6 thirty, smart machine can open NFC
Function, remaining time close NFC function, so can achieve the purpose for saving smart machine power consumption.
The present invention also provides a kind of one embodiment of the power consumption optimization method of smart machine, as shown in Figure 2, comprising:
S1 smart machine obtains historical behavior operation of the user on the smart machine；
Smart machine provided by the invention includes the intelligence such as smart phone, Intelligent flat computer, Intelligent bracelet, smartwatch
Equipment.When user uses these smart machines, smart machine will record the behavior operation of user, history of forming behavior operation note
Record.
By taking smartwatch as an example, such as user has used positioning function in 09 second 30 minutes at 07 point of September in 2018 21 days,
16 points of September in 2018 21 days also use positioning function in 04 second 33 minutes ...
Smart machine can be grasped in the behavior operation for predicting future time section from the historical behavior of user on intelligent devices
Middle acquisition historical behavior of noting down operates.
Smart machine described in S21 is operated according to historical behavior of the user on the smart machine and hidden Markov
Model predicts behavior operation of the user in future time section.
Hidden Markov model (HMM) can usually be described with five members, including 2 state sets and 3 probability matrixs:
1. hidden state S.
Meet Markov property between these states, is the practical state implied in Markov model.These shapes
State can not usually be obtained by directly observing.
2. Observable state O.
It is associated with hidden state in a model, it can be obtained by directly observing.(such as O1, O2, O3 etc., it is considerable
The number of survey state is not necessarily intended to consistent with the number of hidden state.)
3. initial state probabilities matrix π
Indicate that hidden state carves the probability matrix of t=1 at the beginning, (such as when t=1, P (S1)=p1, P (S2)=P2,
P (S3)=p3, then initial state probabilities matrix π=[p1p2p3].
4. hidden state transition probability matrix A.
Describe the transition probability in HMM model between each state.
Wherein Aij=P (Sj | Si), 1≤i, j≤N.
It indicates under conditions of t moment, state are Si, is the probability of Sj in t+1 moment state.
5. observation state transition probability matrix B (the entitled Confusion Matrix of English, literal translate for confusion matrix it is less easy
In from literal understanding).
N is enabled to represent hidden state number, M represents Observable state number, then:
Bij=P (Oi | Sj), 1≤i≤M, 1≤j≤N.
It indicates under the conditions of t moment, hidden state are Sj, observation state is the probability of Oi.
In general, succinct one hidden Markov model of expression can be carried out with λ=(A, B, π) triple.Hidden Ma Erke
Husband's model is actually the extension of standard Markov model, is added to Observable state set and these states and hidden state
Between probabilistic relation.
Using Hidden Markov Model, prediction user can be operated according to historical behavior of the user on the smart machine
Behavior operation in future time section.
Such as user is student, positioning function has been used within 09 second 30 minutes at 07 point of September in 2018 21 days, in September, 2018
21 days 16 points also use positioning function within 04 second 33 minutes, this is the Observable state that can intuitively obtain；018 on September 21,07
Time for school at point 30 minutes and 09 second, 16 points of September in 2018 21 days 33 minutes and 04 second are classes are over time, time for school and classes are over that the time is
For hidden state；By hidden Markov model, it can be predicted user in the positioning function service condition of future time section.
Smart machine described in S3 is operated according to behavior of the user in future time section, control on smart machine with the row
For the switch state for operating correlation function.
After predicting behavior operation of the user in future time section, i.e., the switch state of controllable correlation function.Example
If prediction result will use positioning function at 16 points to 16 thirty for user, positioning function can be opened within the time period,
Remaining period closes positioning function.
The present invention provides a kind of one embodiment of the power consumption optimization method of smart machine, as shown in Figure 3, comprising:
S1 smart machine obtains historical behavior operation of the user on the smart machine.
Smart machine provided by the invention includes the intelligence such as smart phone, Intelligent flat computer, Intelligent bracelet, smartwatch
Equipment.When user uses these smart machines, smart machine will record the behavior operation of user, history of forming behavior operation note
Record.
By taking smartwatch as an example, such as user has used positioning function in 09 second 30 minutes at 07 point of September in 2018 21 days,
16 points of September in 2018 21 days also use positioning function in 04 second 33 minutes ...
Smart machine described in S21 is operated according to historical behavior of the user on the smart machine and hidden Markov
Model predicts behavior operation of the user in future time section.
Smart machine described in S31 is operated according to behavior of the user in future time section, and systematic function switchs list, described
Functional switch list includes function title and opening time section, shut-in time section, as shown in the table:
Table 1
| Function title | Opening time section | Shut-in time section |
| Positioning | 7 points~8 points；16 points~17 points | 8 points~16 points；7 points of 17 points~next day |
| NFC | 18 points~19 points | 18 points of 19 points~next day |
| GPRS | 18 points~22 points | 18 points of 22 points~next day |
Smart machine described in S32 controls related to behavior operation on smart machine according to the functional switch list
The switch state of function.Corresponding function is opened or closed in the corresponding period.
As shown in figure 4, the present invention provides a kind of one embodiment of the power consumption optimization method of smart machine, comprising:
S1 smart machine obtains historical behavior operation of the user on the smart machine；
Smart machine provided by the invention includes the intelligence such as smart phone, Intelligent flat computer, Intelligent bracelet, smartwatch
Equipment.When user uses these smart machines, smart machine will record the behavior operation of user, history of forming behavior operation note
Record.
By taking smartwatch as an example, such as user has used positioning function in 09 second 30 minutes at 07 point of September in 2018 21 days,
16 points of September in 2018 21 days also use positioning function in 04 second 33 minutes ...
Smart machine can be grasped in the behavior operation for predicting future time section from the historical behavior of user on intelligent devices
Middle acquisition historical behavior of noting down operates.
Smart machine described in S2 is operated according to historical behavior of the user on the smart machine, predicts user at future
Between behavior operation in section；
Smart machine can get all operation notes in historical time section, including all functions of using, and it is every
One function is corresponding to use the time.Smart machine can predict user in the sexual behaviour of future time section according to these records
Operation.
Such as in user daily at 7 points to 8 points, 16 points to 17 points the two periods are all using positioning function, then may be used
User is obtained at 7 points to 8 points with prediction, and 16 points to 17 points the two periods can all use positioning function.
Smart machine described in S3 is operated according to behavior of the user in future time section, control on smart machine with the row
For the switch state for operating correlation function.
After predicting behavior operation of the user in future time section, i.e., the switch state of controllable correlation function.Example
If prediction obtains user at 7 points to 8 points, after 16 points to 17 points the two periods can all use positioning function, arrived whenever the time
Positioning function can be opened when up to 7, when then reaching at 8, positioning function can be closed, is opened again when reaching at 16 and positions function
Can, positioning function can be closed when reaching at 17.It can be seen that only opening positioning function there are two hour in one day time
Can, positioning function is opened relative to past 24 hours whole day, considerable battery level can be saved.
For another example the network operations such as wechat, webpage are all when in the historical behavior operation note of user on intelligent devices
When beginning to use after 18 points, then can predict that user in future time section, also can just use network after 18 points
Operating function, therefore before 18 points, can close the network communicating functions such as GPRS in the running on backstage, after 18 points,
The network communicating functions such as GPRS are just opened in the running on backstage.
In another example there is NFC on many smart phones now, (NearField Communication, near radio are logical
Letter) function, subway may be implemented by NFC function and swipe the card.Smart machine gets 8 points to 8 of morning of user's every workday
Thirty in the two periods of 6 pm to 6 thirty, can all use NFC function brush subway, then can predict following
8 points of morning of every workday to 8 thirty, the two periods of 6 pm to 6 thirty, user can use NFC function, because
This, in 8 points of morning of every workday to 8 thirty, the two periods of 6 pm to 6 thirty, smart machine opens NFC function,
Remaining time closes NFC function, so can achieve the purpose for saving smart machine power consumption.
Historical behavior operation of the user on the smart machine is uploaded to server by smart machine described in S41；
Server described in S42 is operated according to behavior of the user in future time section, and systematic function switchs list, and by institute
It states functional switch list and is sent to another smart machine.
In the present solution, smart machine, which can also operate the historical behavior of user on intelligent devices, is uploaded to server,
It can be regarded as putting on record on the server, server can also be operated according to behavior of the user in future time section, generate function
List can be switched.When user has demand, another equipment can be sent to from server end by the functional switch list that prediction generates
In, low-power consumption work can be realized in another equipment.
Such as when user has purchased new smartwatch, since there is no relevant historical behaviors to record for smartwatch,
Direct export function list can be switched from server, then according to functional switch list, modify the configuration text of correlation function
Part realizes low-power consumption running on new equipment.
As shown in figure 5, the present invention also provides a kind of embodiment of smart machine, the smart machine includes:
Behavior obtains module, for obtaining historical behavior operation of the user on the smart machine.
Behavior prediction module is operated according to historical behavior of the user on the smart machine, predicts user at future
Between behavior operation in section.
Functional control module, for being operated according to behavior of the user in future time section, control on smart machine with institute
State the switch state of behavior operation correlation function.
The volume of the smart machines such as existing Intelligent bracelet, smartwatch is smaller, can configure battery space with
Capacity is also relatively small, and as function is more and more abundant, battery capacity has been increasingly difficult to meet the needs of using, in battery
In the case that capacity is constant, to keep battery life longer, unnecessary battery consumption is reduced, saving electricity becomes one kind
Feasible scheme.
But in current optimised power consumption scheme, the energy optimization scheme in some smart machines is divided into daytime and evening
Two periods, daytime open certain functions, have arrived and these functions are closed at night.User, which can choose, opens this energy optimization
Scheme achievees the purpose that device energy conservation.But the prioritization scheme of this scheme is relatively simple, due to user use habit not
Together, the function point for consuming electricity is also different, and smart machine is not operated according to the behavior of user, provides personalized energy-saving square
Case, battery use the distribution not optimized, and phone watch cell electricity must not get well good utilization.
Smart machine provided by the invention includes the intelligence such as smart phone, Intelligent flat computer, Intelligent bracelet, smartwatch
Equipment.When user uses these smart machines, smart machine will record the behavior operation of user, history of forming behavior operation note
Record.
By taking smartwatch as an example, such as user has used positioning function in 09 second 30 minutes at 07 point of September in 2018 21 days,
16 points of September in 2018 21 days also use positioning function in 04 second 33 minutes ...
Smart machine can be grasped in the behavior operation for predicting future time section from the historical behavior of user on intelligent devices
It notes down middle acquisition historical behavior operation note, including all functions of using and each function is corresponding uses the time.So
Smart machine can be according to these records afterwards, and the sexual behaviour for predicting user in future time section operates.
Such as in user daily at 7 points to 8 points, 16 points to 17 points the two periods are all using positioning function, then may be used
User is obtained at 7 points to 8 points with prediction, and 16 points to 17 points the two periods can all use positioning function.
After predicting behavior operation of the user in future time section, i.e., the switch state of controllable correlation function.Example
If prediction obtains user at 7 points to 8 points, after 16 points to 17 points the two periods can all use positioning function, arrived whenever the time
Positioning function can be opened when up to 7, when then reaching at 8, positioning function can be closed, is opened again when reaching at 16 and positions function
Can, positioning function can be closed when reaching at 17.It can be seen that only opening positioning function there are two hour in one day time
Can, positioning function is opened relative to past 24 hours whole day, considerable battery level can be saved.
For another example the network operations such as wechat, webpage are all when in the historical behavior operation note of user on intelligent devices
When beginning to use after 18 points, then can predict that user in future time section, also can just use network after 18 points
Operating function, therefore before 18 points, can close the network communicating functions such as GPRS in the running on backstage, after 18 points,
The network communicating functions such as GPRS are just opened in the running on backstage.
In another example there is NFC on many smart phones now, (NearField Communication, near radio are logical
Letter) function, subway may be implemented by NFC function and swipe the card.Smart machine gets 8 points to 8 of morning of user's every workday
Thirty in the two periods of 6 pm to 6 thirty, can all use NFC function brush subway, then can predict following
8 points of morning of every workday to 8 thirty, the two periods of 6 pm to 6 thirty, user can use NFC function, because
This, in 8 points of morning of every workday to 8 thirty, the two periods of 6 pm to 6 thirty, smart machine opens NFC function,
Remaining time closes NFC function, so can achieve the purpose for saving smart machine power consumption.
As shown in fig. 6, the present invention also provides a kind of embodiment of smart machine, the smart machine includes:
Behavior obtains module, for obtaining historical behavior operation of the user on the smart machine.
The behavior prediction module is also used to the historical behavior operation according to user on the smart machine, Yi Jiyin
Markov model predicts behavior operation of the user in future time section.
First list generation module, for being operated according to behavior of the user in future time section, systematic function switch train
Table, the functional switch list include function title and opening time section, shut-in time section.
Functional control module, for controlling and operating phase with the behavior on smart machine according to the functional switch list
Close the switch state of function.
Smart machine provided by the invention includes the intelligence such as smart phone, Intelligent flat computer, Intelligent bracelet, smartwatch
Equipment.When user uses these smart machines, smart machine will record the behavior operation of user, history of forming behavior operation note
Record.
By taking smartwatch as an example, such as user has used positioning function in 09 second 30 minutes at 07 point of September in 2018 21 days,
16 points of September in 2018 21 days also use positioning function in 04 second 33 minutes ...
Smart machine can be grasped in the behavior operation for predicting future time section from the historical behavior of user on intelligent devices
Middle acquisition historical behavior of noting down operates.Then smart machine is operated according to historical behavior of the user on the smart machine,
And hidden Markov model, predict behavior operation of the user in future time section.
Hidden Markov model (HMM) can usually be described with five members, including 2 state sets and 3 probability matrixs:
1. hidden state S.
Meet Markov property between these states, is the practical state implied in Markov model.These shapes
State can not usually be obtained by directly observing.
2. Observable state O.
It is associated with hidden state in a model, it can be obtained by directly observing.(such as O1, O2, O3 etc., it is considerable
The number of survey state is not necessarily intended to consistent with the number of hidden state.)
3. initial state probabilities matrix π
Indicate that hidden state carves the probability matrix of t=1 at the beginning, (such as when t=1, P (S1)=p1, P (S2)=P2,
P (S3)=p3, then initial state probabilities matrix π=[p1p2p3].
4. hidden state transition probability matrix A.
Describe the transition probability in HMM model between each state.
ai,j=p (Sj:t+1|Si:t),i≥1,j≤m
It indicates under conditions of t moment, state are Si, is the probability of Sj in t+1 moment state.
5. observation state transition probability matrix B (the entitled Confusion Matrix of English, literal translate for confusion matrix it is less easy
In from literal understanding).
N is enabled to represent hidden state number, M represents Observable state number, then:
bi,j=p (Oj:t+1|Oi:t),1≤i≤n,1≤j≤m
It indicates under the conditions of t moment, hidden state are Sj, observation state is the probability of Oi.
In general, succinct one hidden Markov model of expression can be carried out with λ=(A, B, π) triple.Hidden Ma Erke
Husband's model is actually the extension of standard Markov model, is added to Observable state set and these states and hidden state
Between probabilistic relation.
Using Hidden Markov Model, prediction user can be operated according to historical behavior of the user on the smart machine
Behavior operation in future time section.
Such as user is student, positioning function has been used within 09 second 30 minutes at 07 point of September in 2018 21 days, in September, 2018
21 days 16 points also use positioning function within 04 second 33 minutes, this is the Observable state that can intuitively obtain；018 on September 21,07
Time for school at point 30 minutes and 09 second, 16 points of September in 2018 21 days 33 minutes and 04 second are classes are over time, time for school and classes are over that the time is
For hidden state；By hidden Markov model, it can be predicted user in the positioning function service condition of future time section.
Smart machine described in S3 is operated according to behavior of the user in future time section, control on smart machine with the row
For the switch state for operating correlation function.
After predicting behavior operation of the user in future time section, the smart machine can be according to user at future
Between behavior operation in section, systematic function switchs list, the functional switch list include function title and opening time section,
Shut-in time section, as shown in the table:
Table 1
| Function title | Opening time section | Shut-in time section |
| Positioning | 7 points~8 points；16 points~17 points | 8 points~16 points；7 points of 17 points~next day |
| NFC | 18 points~19 points | 18 points of 19 points~next day |
| GPRS | 18 points~22 points | 18 points of 22 points~next day |
Smart machine controls according to the functional switch list and operates opening for correlation function with the behavior on smart machine
Off status.Corresponding function is opened or closed in the corresponding period.
The present invention also provides a kind of optimised power consumption system of smart machine, system includes:
Behavior obtains module, for obtaining historical behavior operation of the user on the smart machine；
Behavior prediction module is operated according to historical behavior of the user on the smart machine, predicts user at future
Between behavior operation in section；
Functional control module, for being operated according to behavior of the user in future time section, control on smart machine with institute
State the switch state of behavior operation correlation function.
The volume of the smart machines such as existing Intelligent bracelet, smartwatch is smaller, can configure battery space with
Capacity is also relatively small, and as function is more and more abundant, battery capacity has been increasingly difficult to meet the needs of using, in battery
In the case that capacity is constant, to keep battery life longer, unnecessary battery consumption is reduced, saving electricity becomes one kind
Feasible scheme.
But in current optimised power consumption scheme, the energy optimization scheme in some smart machines is divided into daytime and evening
Two periods, daytime open certain functions, have arrived and these functions are closed at night.User, which can choose, opens this energy optimization
Scheme achievees the purpose that device energy conservation.But the prioritization scheme of this scheme is relatively simple, due to user use habit not
Together, the function point for consuming electricity is also different, and smart machine is not operated according to the behavior of user, provides personalized energy-saving square
Case, battery use the distribution not optimized, and phone watch cell electricity must not get well good utilization.
Smart machine provided by the invention includes the intelligence such as smart phone, Intelligent flat computer, Intelligent bracelet, smartwatch
Equipment.When user uses these smart machines, smart machine will record the behavior operation of user, history of forming behavior operation note
Record.
By taking smartwatch as an example, such as user has used positioning function in 09 second 30 minutes at 07 point of September in 2018 21 days,
16 points of September in 2018 21 days also use positioning function in 04 second 33 minutes ...
Smart machine can be grasped in the behavior operation for predicting future time section from the historical behavior of user on intelligent devices
It notes down the operation of middle acquisition historical behavior, including all functions of using and each function is corresponding uses the time.Intelligence is set
It is standby user to be predicted in the sexual behaviour operation of future time section according to these records.
Such as in user daily at 7 points to 8 points, 16 points to 17 points the two periods are all using positioning function, then may be used
User is obtained at 7 points to 8 points with prediction, and 16 points to 17 points the two periods can all use positioning function.
After predicting behavior operation of the user in future time section, i.e., the switch state of controllable correlation function.Example
If prediction obtains user at 7 points to 8 points, after 16 points to 17 points the two periods can all use positioning function, arrived whenever the time
Positioning function can be opened when up to 7, when then reaching at 8, positioning function can be closed, is opened again when reaching at 16 and positions function
Can, positioning function can be closed when reaching at 17.It can be seen that only opening positioning function there are two hour in one day time
Can, positioning function is opened relative to past 24 hours whole day, considerable battery level can be saved.
For another example the network operations such as wechat, webpage are all when in the historical behavior operation note of user on intelligent devices
When beginning to use after 18 points, then can predict that user in future time section, also can just use network after 18 points
Operating function, therefore before 18 points, can close the network communicating functions such as GPRS in the running on backstage, after 18 points,
The network communicating functions such as GPRS are just opened in the running on backstage.
In another example there is NFC on many smart phones now, (NearField Communication, near radio are logical
Letter) function, subway may be implemented by NFC function and swipe the card.Smart machine gets 8 points to 8 of morning of user's every workday
Thirty in the two periods of 6 pm to 6 thirty, can all use NFC function brush subway, then can predict following
8 points of morning of every workday to 8 thirty, the two periods of 6 pm to 6 thirty, user can use NFC function, because
This, in 8 points of morning of every workday to 8 thirty, the two periods of 6 pm to 6 thirty, smart machine opens NFC function,
Remaining time closes NFC function, so can achieve the purpose for saving smart machine power consumption.
As shown in fig. 7, the present invention also provides a kind of another embodiment of the optimised power consumption system of smart machine, system
Include:
Behavior obtains module, for obtaining historical behavior operation of the user on the smart machine；
Behavior prediction module is operated according to historical behavior of the user on the smart machine, predicts user at future
Between behavior operation in section；
First list generation module, for being operated according to behavior of the user in future time section, systematic function switch train
Table, the functional switch list include function title and opening time section, shut-in time section；
The functional control module, for controlling and being grasped on smart machine with the behavior according to the functional switch list
Make the switch state of correlation function.
The smart machine further include: transmission module, for the historical behavior by the user on the smart machine
Operation is uploaded to server；
The optimised power consumption system of the smart machine further includes server, and the server includes: that second list generates mould
Block, for being operated according to behavior of the user in future time section, systematic function switchs list, and by the functional switch list
It is sent to another smart machine.
Smart machine provided by the invention includes the intelligence such as smart phone, Intelligent flat computer, Intelligent bracelet, smartwatch
Equipment.When user uses these smart machines, smart machine will record the behavior operation of user, history of forming behavior operation note
Record.
By taking smartwatch as an example, such as user has used positioning function in 09 second 30 minutes at 07 point of September in 2018 21 days,
16 points of September in 2018 21 days also use positioning function in 04 second 33 minutes ...
Smart machine can be grasped in the behavior operation for predicting future time section from the historical behavior of user on intelligent devices
Middle acquisition historical behavior of noting down operates, and then smart machine is operated according to historical behavior of the user on the smart machine,
And hidden Markov model, predict behavior operation of the user in future time section.
Hidden Markov model (HMM) can usually be described with five members, including 2 state sets and 3 probability matrixs:
1. hidden state S.
Meet Markov property between these states, is the practical state implied in Markov model.These shapes
State can not usually be obtained by directly observing.(such as S1, S2, S3 etc..)
2. Observable state O.
It is associated with hidden state in a model, it can be obtained by directly observing.(such as O1, O2, O3 etc., it is considerable
The number of survey state is not necessarily intended to consistent with the number of hidden state.)
3. initial state probabilities matrix π
Indicate that hidden state carves the probability matrix of t=1 at the beginning, (such as when t=1, P (S1)=p1, P (S2)=P2,
P (S3)=p3, then initial state probabilities matrix π=[p1p2p3].
4. hidden state transition probability matrix A.
Describe the transition probability in HMM model between each state.
ai,j=p (Sj:t+1|Si:t),i≥1,j≤m
It indicates under conditions of t moment, state are Si, is the probability of Sj in t+1 moment state.
5. observation state transition probability matrix B (the entitled Confusion Matrix of English, literal translate for confusion matrix it is less easy
In from literal understanding).
N is enabled to represent hidden state number, M represents Observable state number, then:
bi,j=p (Oj:t+1|Oi:t),1≤i≤n,1≤j≤m
It indicates under the conditions of t moment, hidden state are Sj, observation state is the probability of Oi.
In general, succinct one hidden Markov model of expression can be carried out with λ=(A, B, π) triple.Hidden Ma Erke
Husband's model is actually the extension of standard Markov model, is added to Observable state set and these states and hidden state
Between probabilistic relation.
Using Hidden Markov Model, prediction user can be operated according to historical behavior of the user on the smart machine
Behavior operation in future time section.
Such as user is student, positioning function has been used within 09 second 30 minutes at 07 point of September in 2018 21 days, in September, 2018
21 days 16 points also use positioning function within 04 second 33 minutes, this is the Observable state that can intuitively obtain；018 on September 21,07
Time for school at point 30 minutes and 09 second, 16 points of September in 2018 21 days 33 minutes and 04 second are classes are over time, time for school and classes are over that the time is
For hidden state；By hidden Markov model, it can be predicted user in the positioning function service condition of future time section.
Smart machine described in S3 is operated according to behavior of the user in future time section, control on smart machine with the row
For the switch state for operating correlation function.
After predicting behavior operation of the user in future time section, the smart machine can be according to user at future
Between behavior operation in section, systematic function switchs list, the functional switch list include function title and opening time section,
Shut-in time section, as shown in the table:
Table 1
| Function title | Opening time section | Shut-in time section |
| Positioning | 7 points~8 points；16 points~17 points | 8 points~16 points；7 points of 17 points~next day |
| NFC | 18 points~19 points | 18 points of 19 points~next day |
| GPRS | 18 points~22 points | 18 points of 22 points~next day |
Smart machine can control according to the functional switch list and operate correlation function with the behavior on smart machine
Switch state.Corresponding function is opened or closed in the corresponding period.
In the present solution, smart machine, which can also operate the historical behavior of user on intelligent devices, is uploaded to server,
It can be regarded as putting on record on the server, server can also be operated according to behavior of the user in future time section, generate function
List can be switched.When user has demand, another equipment can be sent to from server end by the functional switch list that prediction generates
In, low-power consumption work can be realized in another equipment.
Such as when user has purchased new smartwatch, since there is no relevant historical behaviors to record for smartwatch,
Direct export function list can be switched from server, then according to functional switch list, modify the configuration text of correlation function
Part realizes low-power consumption running on new equipment.
It should be noted that above-described embodiment can be freely combined as needed.The above is only of the invention preferred
Embodiment, it is noted that for those skilled in the art, in the premise for not departing from the principle of the invention
Under, several improvements and modifications can also be made, these modifications and embellishments should also be considered as the scope of protection of the present invention.
Claims (10)
1. a kind of power consumption optimization method of smart machine characterized by comprising
Smart machine obtains historical behavior operation of the user on the smart machine；
The smart machine is operated according to historical behavior of the user on the smart machine, predicts user in future time section
Behavior operation；
The smart machine is operated according to behavior of the user in future time section, is controlled and is operated on smart machine with the behavior
The switch state of correlation function.
2. a kind of power consumption optimization method of smart machine according to claim 1, which is characterized in that the smart machine root
According to historical behavior operation of the user on the smart machine, behavior operation of the user in future time section is predicted, it is specific to wrap
It includes:
The smart machine is operated according to historical behavior of the user on the smart machine and hidden Markov model, in advance
Survey behavior operation of the user in future time section.
3. a kind of power consumption optimization method of smart machine according to claim 1, which is characterized in that the smart machine root
According to behavior operation of the user in future time section, the switch shape on smart machine with behavior operation correlation function is controlled
State specifically includes:
The smart machine is operated according to behavior of the user in future time section, and systematic function switchs list, and the function is opened
Closing list includes function title and opening time section, shut-in time section；
The smart machine controls according to the functional switch list and operates opening for correlation function with the behavior on smart machine
Off status.
4. a kind of power consumption optimization method of smart machine according to claim 1, which is characterized in that further include:
Historical behavior operation of the user on the smart machine is uploaded to server by the smart machine；
The server is operated according to behavior of the user in future time section, and systematic function switchs list, and by the function
Switch list is sent to another smart machine.
5. a kind of smart machine characterized by comprising
Behavior obtains module, for obtaining historical behavior operation of the user on the smart machine；
Behavior prediction module is operated according to historical behavior of the user on the smart machine, predicts user in future time section
Interior behavior operation；
Functional control module, for being operated according to behavior of the user in future time section, control on smart machine with the row
For the switch state for operating correlation function.
6. a kind of smart machine according to claim 5, it is characterised in that:
The behavior prediction module is also used to historical behavior operation and hidden Ma Er according to user on the smart machine
Can husband's model, predict user in future time section behavior operation.
7. a kind of smart machine according to claim 5, it is characterised in that:
First list generation module, for being operated according to behavior of the user in future time section, systematic function switchs list, institute
Stating functional switch list includes function title and opening time section, shut-in time section；
The functional control module, for controlling and operating phase with the behavior on smart machine according to the functional switch list
Close the switch state of function.
8. a kind of optimised power consumption system of smart machine, which is characterized in that the smart machine includes:
Behavior obtains module, for obtaining historical behavior operation of the user on the smart machine；
Behavior prediction module is operated according to historical behavior of the user on the smart machine, predicts user in future time section
Interior behavior operation；
Functional control module, for being operated according to behavior of the user in future time section, control on smart machine with the row
For the switch state for operating correlation function.
9. a kind of optimised power consumption system of smart machine according to claim 8, it is characterised in that:
First list generation module, for being operated according to behavior of the user in future time section, systematic function switchs list, institute
Stating functional switch list includes function title and opening time section, shut-in time section；
The functional control module, for controlling and operating phase with the behavior on smart machine according to the functional switch list
Close the switch state of function.
10. a kind of optimised power consumption system of smart machine according to claim 8, it is characterised in that:
The smart machine further include:
Transmission module, for historical behavior operation of the user on the smart machine to be uploaded to server；
The optimised power consumption system of the smart machine further includes server, and the server includes:
Second list generation module, for being operated according to behavior of the user in future time section, systematic function switchs list, and
The functional switch list is sent to another smart machine.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110007747A (en) * | 2019-03-29 | 2019-07-12 | 联想(北京)有限公司 | A kind of control method and control device |
| WO2021042368A1 (en) * | 2019-09-06 | 2021-03-11 | 阿里巴巴集团控股有限公司 | Power consumption control and scheme generation method, device, system, and storage medium |
| CN116225493A (en) * | 2023-02-15 | 2023-06-06 | 广东壹健康健康产业集团股份有限公司 | Intelligent hardware upgrade firmware configuration generation method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107277906A (en) * | 2017-07-28 | 2017-10-20 | 广东欧珀移动通信有限公司 | Mode selecting method, device, terminal and computer-readable recording medium |
| CN107402921A (en) * | 2016-05-18 | 2017-11-28 | 阿里巴巴集团控股有限公司 | Identify event-order serie data processing method, the apparatus and system of user behavior |
| CN107783801A (en) * | 2017-11-06 | 2018-03-09 | 广东欧珀移动通信有限公司 | Application program forecast model is established, preloads method, apparatus, medium and terminal |
| CN108134691A (en) * | 2017-12-18 | 2018-06-08 | 广东欧珀移动通信有限公司 | Model building method, Internet resources preload method, apparatus, medium and terminal |
-
2018
- 2018-09-26 CN CN201811126852.XA patent/CN109388224A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107402921A (en) * | 2016-05-18 | 2017-11-28 | 阿里巴巴集团控股有限公司 | Identify event-order serie data processing method, the apparatus and system of user behavior |
| CN107277906A (en) * | 2017-07-28 | 2017-10-20 | 广东欧珀移动通信有限公司 | Mode selecting method, device, terminal and computer-readable recording medium |
| CN107783801A (en) * | 2017-11-06 | 2018-03-09 | 广东欧珀移动通信有限公司 | Application program forecast model is established, preloads method, apparatus, medium and terminal |
| CN108134691A (en) * | 2017-12-18 | 2018-06-08 | 广东欧珀移动通信有限公司 | Model building method, Internet resources preload method, apparatus, medium and terminal |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110007747A (en) * | 2019-03-29 | 2019-07-12 | 联想(北京)有限公司 | A kind of control method and control device |
| CN110007747B (en) * | 2019-03-29 | 2021-11-16 | 联想(北京)有限公司 | Control method and control device |
| WO2021042368A1 (en) * | 2019-09-06 | 2021-03-11 | 阿里巴巴集团控股有限公司 | Power consumption control and scheme generation method, device, system, and storage medium |
| CN113728294A (en) * | 2019-09-06 | 2021-11-30 | 阿里巴巴集团控股有限公司 | Power consumption control and scheme generation method, device, system and storage medium |
| CN116225493A (en) * | 2023-02-15 | 2023-06-06 | 广东壹健康健康产业集团股份有限公司 | Intelligent hardware upgrade firmware configuration generation method |
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